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  • CHANGE DETECTION IN PHOTOGRAMMETRIC POINT CLOUDS FOR MONITORING OF ALPINE, GRAVITATIONAL MASS MOVEMENTS

    A. Dinkel, L. Hoegner∗, A. Emmert, L. Raffl, U. Stilla

    Photogrammetry and Remote Sensing, Technical University of Munich, 80290 Munich, Germany - agnes.dinkel@tum.de, ludwig.hoegner@tum.de, adrian.emmert@tum.de, lukas.raffl@tum.de, stilla@tum.de

    Commission II

    KEY WORDS: Bundle block adjustment, optical images, photogrammetric point cloud, change detection, crevices

    ABSTRACT:

    This contribution discusses the accuracy and the applicability of Photogrammetric point clouds based on dense image matching for the monitoring of gravitational mass movements caused by crevices. Four terrestrial image sequences for three different time epochs have been recorded and oriented using ground control point in a local reference frame. For the first epoch, two sequences are recorded, one in the morning and one in the afternoon to evaluate the noise level within the point clouds for a static geometry and changing light conditions. The second epoch is recorded a few months after the first epoch where also no significant change has occurred in between. The third epoch is recorded after one year with changes detected. As all point clouds are given in the same local coordinate frame and thus are co-registered via the ground control points, change detection is based on calculating the Multiscale-Model-to-Model-Cloud distances (M3C2) of the point clouds. Results show no movements for the first year, but identify significant movements comparing the third epoch taken in the second year. Besides the noise level estimation, the quality checks include the accuracy of the camera orientations based on ground control points, the covariances of the bundle adjustment, and a comparison the Geodetic measurements of additional control points and a laser scanning point cloud of a part of the crevice. Additionally, geological measurements of the movements have been performed using extensometers.

    1. INTRODUCTION

    One of the direct consequences of climate change is the melt- ing of permafrost in alpine areas. In combination with extreme weather especially heavy rain falls gravitational mass movements in the mountains like rockfalls and hang slides are going to be a more frequently and more dangerous hazard (Clague and Stead, 2012). An important task in predicting these mass movements is the classification of the movement type based on a set of indicat- ors. Hungr et al. (2014) classifies five different movement types: fall, topple, slide, spread, and flow. This classification needs a detailed observation of movements. Here, observing the move- ment rate and direction of single points over time is not sufficient to model complex changes. This modeling requires the observa- tion of a various number of 3D points for longer time periods and leads to a classification of the movement type and an estimation of possible moving masses.

    Different measurement techniques are used for this purpose. Geological systems like extensometers (Malet et al., 2002; Mas- sey et al., 2013) are used to measure changes in the width of crevices. Geodetic measurements are mainly based on tachy- meter, GNSS receivers (Squarzoni et al., 2005), radar interfer- ometry or laser scanning (Roberti et al., 2018; Delacourt et al., 2007; Kasperski et al., 2010; Oppikofer et al., 2009). All these devices have in common their high costs compared to cameras and the need of professional personnel. Surveillance of larger areas or all potential risk areas in the Alps is more or less im- possible in that way. Using cameras allows handing over the job of data acquisition to non-professionals (Urban et al., 2019; Romeo et al., 2019; Mayr et al., 2019; Peppa et al., 2019; Kromer et al., 2019; Esposito et al., 2017; Hendrickx et al., 2019; Warrick ∗ Corresponding author

    et al., 2016; Piermattei et al., 2016; Eltner et al., 2016; Stumpf et al., 2015; Kääb et al., 2014; Westoby et al., 2012).

    This contribution focuses on the assessment of possible ac- curacies in 3D reconstruction and change detection based on pho- togrammetric image sets. Photogrammetric results are compared to Geodetic measurements using tachymeter and laser scanner.

    2. MEASUREMENT AND PROCESSING STRATEGY

    The evaluation of the accuracy and reliability in 3D reconstruc- tion and change detection and deformation analysis is split in two parts. In the first part, the accuracy of the 3D point generation is evaluated. The second part focuses on the change detection between two point clouds.

    A set of ground control points defines a local coordinate system. Camera orientations are unknown beforehand and the image set is oriented based on the ground control points. Two different sets of ground control points are used. Set one is fixed longterm ground control points defining the local coordinate system for registering the different epochs. Set two is ground control points that were individually measured for every epoch. The interior orientation of the camera is given and assumed to be fix for one epoch.

    2.1 3D reconstruction

    The 3D reconstruction is based on the well-known two step process of bundle adjustment of the image based on tie point and ground control points (Förstner and Wrobel, 2016) fol- lowed by a semi-global matching for dense point cloud gener- ation (Hirschmueller, 2008). Measurements of crevices are taken with an image overlap of at least 80%. The camera is moved

    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-2-2020, 2020 XXIV ISPRS Congress (2020 edition)

    This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-V-2-2020-687-2020 | © Authors 2020. CC BY 4.0 License.

    687

  • Figure 1. Double-edge configuration.

    Figure 2. Crevice configuration.

    long the crevice in parallel view. The recording positions have been simulated beforehand with the calibrated camera. For large crevices with several meters width, two different camera orienta- tion schemes are used:

    • Double-edge configuration (Fig. 1): The images are ordered parallel to the crevice so that both edges of the crevice are visible in every image. The position of the camera moves parallel to the pathway of the crevice.

    • Crevice configuration (Fig. 2): The camera is tilted over the edge of the crevice to record the scene in the interior of the crevice. Images now show only few or even no ground control points and are connected to the first image set via tie points. Here, the camera is also moving parallel to the pathway of the crevice.

    In such cases with large crevices, it is likely that the moving side of the crevice is already unstable and cannot be accessed directly. For small crevices, both sides of the crevice are often accessible, but the crevice is to small to put the camera inside.

    The bundle adjustment of the camera orientations uses image points and ground control points as observations, the interior ori- entation is assumed to be fixed. The exterior orientation paramet- ers are the unknowns. As the collinearity equations (Förstner and Wrobel, 2016) lead to a non-linear equation system in the bundle adjustment, an iterative adjustment has to be done and initial val- ues for the unknown parameters of the exterior orientation are estimated from the ground control points and their image points.

    Three strategies are used to estimate the initial exterior orienta- tion. If at least six ground control points are visible, the direct lin- ear transform (DLT) is used (Luhmann, 2018). For three to five ground control points, the minimal-object-information strategy (MOI) (Luhmann, 2018) is used. For images with less than three ground control points, the two neighboring images with estimated exterior orientation are used to interpolate their exterior orienta- tion parameters linear from these two neighbors with the estim- ated image overlaps as weights. For five and four ground control points in the MOI strategy, the optimal set of three ground con- trol points is found by searching the combination that spans the largest triangle in the image.

    The bundle adjustment uses variance components to model the different accuracy levels of measured image points and measured ground control points. Random Sample Consensus (RANSAC) (Fischler and Bolles, 1981) removes outliers in the tie point search before the bundle adjustment is performed. The resulting exterior orientations are the input for the semi-global matching (Hirschmueller, 2008).

    2.2 Change detection and deformation analysis

    Change detection consists of three major steps: i) coregistration; ii) noise detection; iii) detection of significant changes. As all image sets are oriented based on the same set of ground control points, one can assume the resulting point clouds to being already registered. An additional coregistration by distance minimization like Iterative Closest Point (Besl and McKay, 1992) would falsify the change detection as it minimizes the distances of all points and thus would also include moved points in the registration.

    The bundle adjustment calculates the inner quality of the resulting adjusted equation system. 3D points are only generated for the tie points and the ground control points. An absolute accuracy of the dense 3D point cloud is not calculated neither in the bundle adjustment nor in the semi-global matching. Estimating the noise level is very important to distinguish noise and real changes.

    The proposed strategy uses the Multiscale-Model-to-Model- Cloud method (M3C2) (Lague et al., 2013) to find for every point in one point cloud the corresponding neighbor